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OPEN Study of the active ingredients and mechanism of Sparganii rhizoma in gastric cancer based on HPLC‑Q‑TOF–MS/MS and network pharmacology Xiaona Lu1,2,3, Yawei Zheng2,3, Fang Wen2, Wenjie Huang2, Xiaoxue Chen2, Shuai Ruan2, Suping Gu2, Yue Hu1,2, Yuhao Teng1,2 & Peng Shu1,2*

Sparganii rhizoma (SL) has potential therapeutic efects on gastric cancer (GC), but its main active ingredients and possible anticancer mechanism are still unclear. In this study, we used HPLC-Q-TOF– MS/MS to comprehensively analyse the chemical components of the aqueous extract of SL. On this basis, a network pharmacology method incorporating target prediction, gene function annotation, and molecular docking was performed to analyse the identifed compounds, thereby determining the main active ingredients and hub genes of SL in the treatment of GC. Finally, the mRNA and protein expression levels of the hub genes of GC patients were further analysed by the Oncomine, GEPIA, and HPA databases. A total of 41 compounds were identifed from the aqueous extract of SL. Through network analysis, we identifed seven main active ingredients and ten hub genes: acacetin, sanleng acid, ferulic acid, methyl 3,6-dihydroxy-2-[(2-hydroxyphenyl) ethynyl]benzoate, cafeic acid, adenine nucleoside, azelaic acid and PIK3R1, PIK3CA, SRC, MAPK1, AKT1, HSP90AA1, HRAS, STAT3, FYN, and RHOA. The results indicated that SL might play a role in GC treatment by controlling the PI3K- Akt and other signalling pathways to regulate biological processes such as proliferation, apoptosis, migration, and angiogenesis in tumour cells. In conclusion, this study used HPLC-Q-TOF–MS/MS combined with a network pharmacology approach to provide an essential reference for identifying the chemical components of SL and its mechanism of action in the treatment of GC.

Gastric cancer (GC) is one of the leading causes of cancer-related death worldwide, and its incidence rate is sixth among ­cancers1. At present, surgery, chemotherapy, and other traditional therapies are the main treatments. How- ever, the incidence of local recurrence and distant metastasis afer gastric cancer surgery is high. Chemotherapy is associated with toxicity and side efects; thus, it is challenging for these treatments to mediate a long-term antitumour efect. Terefore, it is necessary to explore new strategies for the treatment of this disease. In China, traditional Chinese medicine (TCM) is widely used in the treatment of GC and has shown advantages with its multipathway, multitarget, and multilink characteristics, small side efects, and signifcant efcacy. Sparganii rhizoma (SL) is the dried tuber of the Sparganiaceae plant Sparganium stoloniferum (Buch.-Ham. ex Graebn.) Buch.-Ham. ex Juz., which is a traditional Chinese medicine. It has a pungent, bitter, fat attributes and enters the liver and spleen meridians. Its efects include tonifying the blood and promoting qi, removing stagnant food, and alleviating pain. It is included in the Pharmacopoeia of the People’s Republic of China (2015 Edition)2. Previous experiments by our research team suggested that the “Sparganii rhizoma-Curcuma zedoary-Salvia chinensis” herb pair with SL as one of the main components had growth-inhibitory efects on both regular and resistant GC cells, and the inhibitory efect increased with increasing ­concentration3. Modern pharmacological studies have also shown that SL has an apparent inhibitory efect on the proliferation of GC cells and can pro- mote tumour cell ­apoptosis4. In addition, some studies have found that the combination of traditional Chinese medicine preparations mainly composed of SL and chemotherapy can prolong the progression-free survival

1Oncology Department, Afliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China. 2First School of Clinical Medicine, Nanjing University of Chinese Medicine, Nanjing, China. 3These authors contributed equally: Xiaona Lu and Yawei Zheng. *email: [email protected]

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(PFS) of patients with advanced gastric cancer, improve the quality of life of patients, and reduce the adverse reactions to chemotherapy­ 5. Although previous studies have shown that SL has potential therapeutic efects on GC, its main active ingredients and possible anticancer mechanism are still unclear. High-performance liquid chromatography coupled with quadrupole time-of-fight mass spectrometry (HPLC-Q-TOF–MS), which is a common qualitative and quantitative analysis technology combining liquid chromatography and mass spectrometry, can be used to analyse the structure of trace components in crude substances without a reference ­substance6. Both positive and negative ionization modes have been used to con- frm the related chemical compounds and their characteristic fragment ions according to the accurate molecular mass information of the excimer ion peaks and the fragment ions. Ten, compounds are ultimately determined by comparisons with the relevant database. HPLC-Q-TOF–MS/MS is characterized by high resolution, high sensitivity, high selectivity, short response time, wide scanning range, high molecular mass accuracy, and an ability to obtain multistage mass spectrum fragment information for compounds. It can quickly analyse and identify the structures of complex substances such as TCM and is very convenient for basic research on TCM materials­ 7,8. Network pharmacology is a method for predicting the pharmacological mechanism of drug treat- ments for diseases based on the theory of systems biology and the use of complex biological network models, starting from the integrity and systematic nature of interactions among drugs, chemical components, targets, and ­diseases9,10. Its holistic and systematic characteristics are consistent with the principles of the holistic view, syndrome diferentiation and treatment of TCM, which have been widely used in the study of ­TCM11,12. For example, Yucheng Guo et al. used a network pharmacology research method to construct a multiscale math- ematical model of infammation-induced tumorigenesis, further identifed the key biological molecular network and genetic interaction module from the dynamic evolution path of infammation and cancer, and predicted the TCM ingredients that can inhibit infammation-induced tumorigenesis. Tis method is of great value for the accurate prevention and treatment of cancer and the modernization of TCM­ 13,14. Terefore, in this study, HPLC- Q-TOF–MS/MS was used to rapidly analyse and identify the chemical components in SL, and the mechanism of SL in the treatment of GC was explored by combining network pharmacology research methods. Te specifc fowchart is shown in Fig. 1. Results Identifcation of the chemical components of SL. We analysed SL aquatic extract samples based on the above conditions of liquid chromatography and mass spectrometry. We used positive and negative ion mode scanning in this paper to obtain as much information as possible. Te exact mass-to-charge ratio (m/z) of the compound was obtained by TOF–MS, while the second-order fragment ion of this mass number was obtained by product ion secondary mass spectrometry. By using online databases, referring to the relevant literature and considering the fragmentation rule of compounds, we qualitatively analysed the structures of SL-related compounds. Forty-one compounds were ultimately identifed: nine phenylpropanoids, eight organic acids, four favonoids, four amino acids, two alkaloids, and fourteen other compounds. Te secondary mass spectra of each compound are shown in the “Supplementary Figures”. Table 1 shows the retention time, mass spectrometry information, and related references of the identifed compounds.

Network pharmacology analysis. Prediction of potential targets of compounds and collection of targets for GC. SwissTargetPrediction predicted a total of 1157 potential targets of the 41 compounds identifed by mass spectrometry, and we obtained 471 afer removing duplicate targets (Supplementary Table S1). We retrieved data from the GeneCards, OMIM, DisGeNET, and TTD databases and identifed 2670, 542, 634, and 3 GC-related targets afer screening, respectively, which resulted in 3225 targets afer merging and removal of duplicate targets (Supplementary Table S2) (Fig. 2a). Potential mapping of the targets of compounds resulted in a total of 262 common targets with those related to GC, which were ultimately identifed as target genes of SL for the treatment of GC (Fig. 2b).

Compound‑target network analysis. We established a compound-target network with 262 GC target genes as anticancer targets (Fig. 3). Tere are 294 nodes and 685 edges in the network, among which the 32 green nodes represent the main components of SL, the 262 orange nodes represent the targets of GC, and the 685 edges represent the interactions between the components and the targets of GC. By observing the network, we found that the same active ingredient can act on multiple targets. Te same target also corresponds to diferent chemi- cal components, which fully refect the multicomponent and multitarget characteristics of SL in GC treatment. According to the network topological parameters, the average values of the degree and betweenness centrality of compound nodes were 21.40625 and 0.076039366, respectively. We screened out compounds with a degree and betweenness centrality greater than the mean, such as acacetin, sanleng acid, ferulic acid, methyl 3.6-dihydroxy- 2-[(2-hydroxyphenyl) ethynyl] benzoate, cafeic acid, adenine nucleoside, and azelaic acid, which may be the main active ingredients of SL in the treatment of GC.

PPI network analysis. Te PPI network reveals the potential connection between targets. Afer removing the free genes, the PPI network contained 222 nodes and 1205 edges, with an average node degree of 10.9 (Fig. 4). Te size and colour of the node refects the importance of the degree. Te larger the degree, the more important the node is in the network, suggesting that it may be a key target of SL in GC treatment. To make the fgure clearer, we used diamonds to highlight the top 20 genes in all nodes. According to the degree value, the top 10 genes were regarded as hub genes, including PIK3R1 (degree = 56), PIK3CA (degree = 56), SRC (degree = 52), MAPK1 (degree = 43), AKT1 (degree = 42), HSP90AA1 (degree = 41), HRAS (degree = 39), STAT3 (degree = 38), FYN (degree = 37), and RHOA (degree = 37).

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Figure 1. Scheme of analysis procedure.

GO analysis and KEGG pathway analysis. To elucidate the molecular mechanism underlying SL efcacy in the treatment of GC, we performed GO and KEGG pathway analyses on 262 anticancer targets (Supplementary Table S3). GO analysis identifed 310 biological processes (BP), 50 cellular components (CC), and 93 molecular

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Quasi- Quasi- molecular (n) molecular (p) [M − H]¯ = [M + Cl/ [M + H]+ = [M + Na]+ COOH]− (Error, MS/MS fragments MS/MS fragments No. Rt (min) (Error, ppm) ppm) Molecular formula (p) (n) Proposed compound References SL1 0.55 104.1072 (2.0) C5H13NO 60, 58 Choline 15 SL2 0.57 175.1184 (− 3.2) C6H14N4O2 130, 116, 70 Arginine 15 SL3 0.62 138.0545 (− 3.4) C7H7NO2 94, 93, 92, 78, 65 Trigonelline 15 119, 94, 92, 77, SL4 0.86 136.0617 (− 2.7) C5H5N5 Adenine 15 67, 65 SL5 0.86 124.0394 (− 2.5) C6H5NO2 106, 80, 78, 53, 52 Nicotinic acid 15 SL6 1.27 113.0346 (− 3.1) C4H4N2O2 96, 95, 70,68,53 Uracil 15 SL7 1.35 117.0195 (1.4) C4H6O4 117, 100, 73 Succinic acid 16 165, 136, 123, 119, SL8 1.65 182.0812 (0.2) C9H11NO3 Tyrosine 15 95, 91, 77 SL9 1.62 132.1015 (− 3.1) C6H13NO2 86, 69, 57, 56 D-Tert-Leucine 16 SL10 2.13 268.1038 (− 1.2) C10H13N5O4 136, 119 Adenine nucleoside 15 149, 137, 123, 121, SL11 4.14 167.0352 (1.3) C8H8O4 Vanillic acid 16 109, 108, 93, 81, 69 SL12 4.19 166.0853 (− 1.5) C9H11NO2 120, 103, 91, 77, 51 Phenylalanine 16 SL13 6.07 137.0249 (3.5) C7H6O3 109, 108, 93, 81, 65 4-Hydroxybenzoic acid 16 SL14 7.37 227.1028 (0.7) C10H14N2O5 209, 181, 116, 84, 70 Carbidopa 17 135, 134, 117, 91, SL15 9.76 179.0349 (5.7) C9H8O4 Cafeic acid 18 71, 59 SL16 10.02 123.0439 (− 1.3) C7H6O2 95, 77, 65, 51 Benzoic acid 16 SL17 10.44 253.0716 (− 0.6) C12H14O6 179, 161, 135, 133 Hwanggeumchal B 19 4-hydroxy-2-methoxyphenyl 383, 343, 301, 283, 1-O-[6-(hydrogen 3-hydroxy- SL18 12.88 445.1336 (− 3.5) C19H26O12 20 139, 125, 124, 99 3-methylpentanedioate)]-β-d- glucopyranoside SL19 13.57 253.0716 (− 0.6) C12H14O6 179, 161, 135, 133 1-Cafeoylglycerol – 2-Propenoic acid, SL20 13.69 237.0764 (− 1.9) C12H14O5 145, 119, 117, 59 3-(4-hydroxyphenyl)-,2,3- 21 dihydroxypropyl ester, (E)- SL21 15.55 163.0404 (2.0) C9H8O3 119, 117, 93 4-Coumaric acid 16 237, 163, 145, 119, SL22 17.72 237.0768 (− 0.2) C12H14O5 1-O-p-coumaroylglycerol 21 117 SL23 17.74 193.0505 (5.0) C10H10O4 178, 134, 133 Ferulic acid 22 252, 193, 175, 160, 2-Propenoic acid, 3(4-hydroxy- SL24 20.88 267.0873 (− 0.4) C13H16O6 149, 134, 133, 3-methoxyphenyl)-,2,3-dihy- 21 105, 77 droxypropyl ester, (2Z)- 252, 193, 175, 160, SL25 21.11 267.0875 (0.3) C13H16O6 149, 134, 133, 1-O-Trans-Feruloylglycerol 21, 23 105, 77 220, 202, 185, 175, SL26 24.37 219.0661 (4.2) C12H12O4 Decarboxy-citrinone 24 167, 147, SL27 29.2 609.1450 (− 1.8) C27H30O16 301, 300, 271, 151 Rutin 16 SL28 29.26 447.1279 (− 1.5) C22H22O10 285, 270, 253 Tilianin 25 187, 169, 125, 9-(2′,3′-Dihydroxypropyloxy)- SL29 29.55 261.1337 (− 2.5) C12H22O6 26 123, 97 9-oxononanoic Acid 187, 169, 143, 125, SL30 30.18 187.0980 (2.2) C9H16O4 Azelaic acid 16 123, 97, 57 315, 314, 300, 299, SL31 32.53 623.1621 (0.5) C28H32O16 Narcissin 16 271, 243 β-d-Glucopyranosiduronic acid, 4-methyl-2-oxo- SL32 35.33 381.1180 (0.0) C18H20O9 177, 145, 117, 89 – 2H-1-benzopyran-7-yl, ethyl ester SL33 35.66 283.0612 (0.0) C16H12O5 268, 239, 224, 211 Acacetin 27 267, 253, 235, 193, SL34 35.75 429.1168 (− 2.8) C22H22O9 179, 161, 149, 135, Feruloyl-cafeoylglycerol 28 134, 133, 117 193, 163, 134, 119, SL35 37.29 413.1224 (− 4.3) C22H22O8 p-Coumaroyl-feruloylglycerol 28 117 237, 219, 163, 145, 1,3-O-Di-trans-p-coumaroyl- SL36 37.42 381.1131 (1.5) C21H20O7 29 119, 117 glycerol SL37 37.43 443.1324 (− 5.3) C23H24O9 193, 134 1,3-O-Diferuloyl glycerol 16 Continued

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Quasi- Quasi- molecular (n) molecular (p) [M − H]¯ = [M + Cl/ [M + H]+ = [M + Na]+ COOH]− (Error, MS/MS fragments MS/MS fragments No. Rt (min) (Error, ppm) ppm) Molecular formula (p) (n) Proposed compound References 267, 249, 235, 219, 1-O-Feruloyl-3-O-p-cou- SL38 37.45 413.1226 (− 1.2) C22H22O8 193, 177, 163, 145, 16 maroylglycerol 134, 119 Methyl 3, 6-dihydroxy-2-[(2- 251, 239, 233, 207, SL39 38.88 283.0605 (− 2.5) C16H12O5 hydroxyphenyl) ethynyl] 16 195, 179, 167, 151 benzoate 327, 309, 291, 239, 9S,12R,13S-Trihydroxy- SL40 40.359 327.21714 (1.6) C18H31O5 229, 221, 211, 183, 30 10E,15Zoctadecadienoic acid 171 329, 311, 229, 211, SL41 41.74 329.2320 (− 4.1) C18H34O5 Sanleng acid 22 209, 193, 183, 171

Table 1. Te compounds identifed of Sparganii rhizoma.

Figure 2. Target maps of Sparganii rhizoma and gastric cancer. (a) Gastric cancer targets in diferent disease databases. (b) Venn diagram of Sparganii rhizoma and gastric cancer targets.

functions (MF). In BP, the targets mainly involve positive regulation of transcription from RNA, negative regula- tion of the apoptotic process, positive regulation of cell proliferation, and positive regulation of cell migration, angiogenesis, and the MAPK cascade. In CC, the targets mainly involve the nucleus, plasma membrane, cyto- plasm, extracellular exosomes, integral components of the plasma membrane, and mitochondria. In MF, the targets mainly involve protein binding, ATP binding, enzyme binding, identical protein binding, protein kinase activity, and protein homodimerization activity. A total of 101 pathways were identifed by KEGG pathway analysis, and the targets were closely related to pathways in cancer, PI3K-Akt signalling pathway, proteoglycans in cancer, microRNAs in cancer, focal adhesion, the Rap1 signalling pathway, the Ras signalling pathway, the cAMP signalling pathway, the HIF-1 signalling pathway, and the MAPK signalling pathway. Tis suggests that SL may play a role in the treatment of GC through the above pathways, among which the PI3K signalling path- way involves 47 potential targets, including most of the hub genes, and may be the key pathway. According to the number of enriched genes, the top 20 results in descending order of enrichment analysis were visualized, as shown in Fig. 5. Te above results indicate that the biological processes involved in the anticancer targets of SL’s main chemical components are diverse and distributed in diferent metabolic pathways, refecting its multipath- way characteristics.

Compound‑target‑pathway network analysis. A compound-target-pathway network was constructed with the targets included in the top 20 pathways and the chemical components corresponding to the targets obtained from KEGG pathway analysis (Fig. 6). Te network contained 181 nodes with 29 representative components, 132 representative targets, 20 representative pathways, and 886 edges. From the diagram of the compound-target- pathway network, we can see intuitively that the targets of SL active components are distributed in diferent pathways, coordinate with each other, and play a common role in the treatment of GC, which comprehensively embodies the multicomponent, multitarget, and multipathway characteristics of traditional Chinese medicine.

Molecular docking analysis. We performed molecular docking analysis on seven major active ingredients with node degree and betweenness centrality greater than the average in the compound-target network and core tar- gets with the top ten degrees in the PPI network. Moreover, the original ligands of potential protein targets were analysed. Afer docking with AutoDock Vina, the obtained data were analysed by a heat map, as shown in Fig. 7.

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Figure 3. Compound-target network. Green elliptical nodes represent chemical components, and orange rectangular nodes represent targets.

It is generally believed that the lower the energy when the conformation of the ligand binding to the receptor is stable, the greater the possibility of action. In this study, almost all active ingredients and core target proteins’ binding energies were less than − 5.0, which indicated that SL active ingredients had better binding activity with core targets, which stated that SL active ingredients had better binding activity with core targets. We selected the docking results of the compound (acacetin) that binds best to the target protein for display (Fig. 8).

External validation of hub genes. mRNA expression levels of hub gens. We used the Oncomine data- base to analyse the diferential expression of hub genes between GC tissues and normal tissues. Te following thresholds were set: p-value: 0.01; fold change: 2; gene rank: Top 10%; data type: mRNA. Te analysis results showed that the mRNA expression of MAPK1 and STAT3 was signifcantly upregulated in GC tissues, and there were no signifcant diferences between GC and normal gastric tissues for other mRNA levels (Fig. 9a). Subse- quently, further validation with the GEPIA database showed that the mRNA levels of MAPK1 and HSP90AA1 were signifcantly upregulated in GC specimens compared with normal gastric specimens (P < 0.01) (Fig. 9b). In addition, we analysed the relationship between hub gene mRNA levels and the pathological stage of GC. Te results showed that the levels of PIK3R1 and HSP90AA1 changed signifcantly with pathological stage and increased signifcantly in stage III (Fig. 9c). Tese results suggested that the expression levels of these two genes might be correlated with GC progression.

Protein expression levels of hub gens. Additionally, we analysed the immunohistochemical staining images in the HPA database to observe the expression levels of hub gene proteins in GC. Te results showed that except for HSP90AA1, the other nine hub genes were expressed to diferent degrees in normal gastric tissues. Compared

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Figure 4. Protein–protein interaction (PPI) network.

with normal gastric tissues, the expression levels of SRC, MAPK1, HSP90AA1, STAT3, and FYN were increased in GC tissues, while the expression of RHOA was decreased in GC tissues (Fig. 10). Discussion In this study, HPLC-Q-TOF–MS/MS technology was used to rapidly and comprehensively analyse the chemical components of SL, and 41 compounds were identifed. Ten, the identifed compounds were studied in network pharmacology. Finally, we found seven main active ingredients in the drug, including acacetin, sanleng acid, ferulic acid, methyl 3.6-dihydroxy-2-[(2-hydroxyphenyl) ethynyl] benzoate, cafeic acid, adenine nucleoside, and azelaic acid; moreover, we identifed PIK3R1, PIK3CA, SRC, MAPK1, AKT1, HSP90AA1, HRAS, STAT3, FYN, and RHOA as hub genes. Molecular docking showed that the active ingredients had good afnity for the hub gene proteins. Tese seven active ingredients may be the material basis for SL to exert therapeutic efcacy for GC. Modern pharmacological studies have shown that acacetin, as a natural favonoid, can resist tumours in multiple links, pathways, and targets and is efective in most tumour cell lines. It can inhibit the prolifera- tion of tumour cells, induce the autophagy and apoptosis of tumour cells, inhibit the invasion and migration of tumour cells and angiogenesis, regulate immunity, and reverse multidrug resistance­ 31. Ferulic acid and caf- feic acid are phenylpropanoids, and their antioxidant properties have been extensively demonstrated. Studies have shown that ferulic acid and cafeic acid can signifcantly inhibit COX-1 and COX-2 enzyme activities and

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Figure 5. Bubble map of GO and KEGG pathway analyses. (a) Biological processes (BP) of GO terms. (b) Cellular components (CC) of GO terms. (c) Molecular functions (MF) of GO terms. (d) KEGG pathway analysis. Bubble size represents the number of enriched genes, and bubble colour diference represents the signifcance of target gene enrichment.

inhibit tumour cell ­proliferation32,33. Ferulic acid can also induce the apoptosis of GC cells by upregulating the tumour suppressor transcription factor p53 and downregulating the mRNA and protein expression levels of the apoptosis inhibitory proteins Survivin and XIAP­ 34,35. Cafeic acid can also cause apoptosis of SCM1 human GC ­cells36. Sanleng acid and azelaic acid are organic acid compounds. Sanleng acid is the earliest organic acid component identifed by SL analysis, but no specifc action mechanism has been reported yet. Azelaic acid can destroy mitochondrial respiration and inhibit cell synthesis; thus, it has good antiproliferation and cytotoxic efects on various cultured tumour cell lines and can be used as a potential anticancer ­drug37. Although methyl 3,6-dihydroxy-2-[(2-hydroxyphenyl) ethynyl] benzoate and adenine nucleoside are the main active ingredients in GC treatment screened by us, there is no clear report on the antitumour efect at present, which deserves further study to discover the potential mechanism of action. An increasing number of studies have shown that TCM is a multitarget drug. Among the ten hub genes identifed in this study, PIK3R1 and PIK3CA were identifed as PI3K/protein kinase B (Akt) signalling pathway regulators. Studies have shown that abnormal upregulation of PIK3R1 and PIK3CA expression enhances the catalytic activity of PI3K and then activates the PI3K-Akt signalling pathway, causing GC cells to overproliferate and increasing the migration and invasion abilities of GC ­cells38–40. Te proto-oncogene c-SRC, a member of the SRC family of kinases (SFKs), is one of the earliest nonreceptor-dependent tyrosine protein kinases found to be closely related to human ­diseases41. Current studies have shown that SRC can promote tumour cell proliferation and tumour angiogenesis, inhibit apoptosis, participate in cancer cell adhesion and invasion, and coregulate tumour growth through the interaction of growth factor receptors and growth ­factors42,43. Mitogen-activated protein kinase 1 (MAPK1) has been confrmed as an essential oncogene in the progression of GC, and its level is elevated in GC tissues and cells, which can promote the proliferation, migration, and invasion of GC cells­ 44–46. Heat shock protein 90 (HSP90) is overexpressed in many malignant tumours, and members of the HSP90 gene family are essential for cell cycle regulation, survival, and apoptosis. Studies have shown that the expression of HSP90AA1 is associated with poor prognosis in ­GC47,48. STAT3, a key transcription factor in tumorigenesis,

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Figure 6. Compound-target-pathway network. Blue circular nodes represent chemical compounds, yellow V-shaped nodes represent targets, and red hexagonal nodes represent pathways.

focuses on multiple signalling pathways, such as cell proliferation, carcinogenesis, and apoptosis, which can promote the growth, proliferation, angiogenesis, metastasis, and immune response of tumour ­cells49,50. Similar to SRC, FYN is an SFK that is overexpressed in GC and is positively correlated with metastasis and may promote gastric cancer metastasis by activating STAT3-mediated epithelial-mesenchymal transition­ 51. In addition, our study also showed that SRC, MAPK1, STAT3, HSP90AA1, PIK3R1, and FYN were overexpressed in GC patients, which may be associated with the poor prognosis of GC patients. Te AKT1 signalling pathway plays a vital role in regulating the biological functions of tumour cell growth, proliferation, apoptosis, and metabolism. Its posi- tive expression rate in GC tissues is signifcantly higher than that in adjacent tissues, and it participates in the occurrence and development of ­GC52–54. HRAS belongs to the RAS gene family, which regulates RAF-MEK-ERK, PI3K/AKT, and other signalling pathways related to cell survival and proliferation by binding to GTP/GDP and the RAS protein to act as a molecular switch­ 55,56. HRAS mutations are closely associated with the occurrence of various tumours. Te expression of RHOA, a RAS homologous family, is related to certain tumorigenesis; how- ever, its prognostic value in GC remains controversial. Some studies have found that the RHOA signaling pathway plays a vital role in the occurrence, invasion, metastasis, immune escape, and multidrug resistance mechanisms of gastric ­cancer57,58. Nevertheless, some studies have shown that the overall prevalence of RHOA-mutant GC is low, usually ofering a lower T stage and no distant ­metastasis59. Our external validation also showed that RHOA was expressed at a low protein level in GC tissues; therefore, further study of this gene is necessary.

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Figure 7. Heat map of molecular docking scores (kcal/mol−1). Ligand represents the original ligand of the protein. SL33, SL41, SL23, SL39, SL15, SL10, and SL30 are acacetin, sanleng acid, ferulic acid, methyl 3,6-dihydroxy-2-[(2-hydroxyphenyl) ethynyl]benzoate, cafeic acid, adenine nucleoside, and azelaic acid, respectively.

Figure 8. Schematic diagram of docking results. Te docking results of acacetin with 10 core target proteins are shown.

To better understand the molecular mechanism of SL in the treatment of GC, we performed GO and KEGG pathway analyses on the targets. GO analysis results showed that the target genes were mainly related to bio- logical processes such as positive regulation of transcription from RNA, negative regulation of the apoptotic process, positive regulation of cell proliferation, positive regulation of cell migration, angiogenesis, and similar processes. In CC, the nucleus accounted for the largest proportion. In MF, protein binding, ATP binding, and enzyme binding were the main components. KEGG pathway analysis showed that the signalling pathway of SL in the treatment of GC was most related to the PI3K-Akt signalling pathway. Additionally, it involved the Ras signalling pathway, the MAPK signalling pathway, and other signalling pathways. Most of the hub genes, such as HRAS, AKT1, HSP90AA1, PIK3CA, PIK3R1, MAPK1, and RHOA, play roles in these signalling pathways, which is consistent with the results of modern pharmacological studies. In conclusion, the analytical method based on HPLC-Q-TOF–MS/MS technology in this study can accu- rately identify the chemical components in SL efciently, rapidly, and comprehensively. Simultaneously, the network pharmacology method is used to deeply excavate its potential active ingredients and the mechanism of drug treatment for GC to provide more scientifc theoretical guidance for the improvement of quality control standards and clinical application of SL in the future. In our study, we found that SL is a multitarget anticancer drug. We predicted that the primary mechanism of action of SL in the treatment of GC is as follows: mediating

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Figure 9. Te mRNA expression levels of hub genes in diferent databases. (a) Oncomine analysis of hub gene mRNA expression levels in diferent cancers. Compared with normal tissues, the red box indicates the overexpression of the target gene in tumour tissues, while the blue box indicates the downregulation of the gene. Te intensity of expression is expressed in shades of colour. (b) Boxplot of hub gene mRNA expression levels in the GEPIA database. Red represents GC tissue, and grey represents normal gastric tissue. (c) Stage plot of hub gene mRNA expression level and pathological stage in the GEPIA database.

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Figure 10. Immunohistochemical images of hub gene protein expression levels in the HPA database.

PI3K-Akt, Ras, MAPK, and other signaling pathways to regulate the proliferation, apoptosis, migration, and angiogenesis of tumour cells, thus playing a role in the treatment of GC. However, the above results still need further experimental verifcation. Methods HPLC‑Q‑TOF–MS/MS analysis. Instruments and materials. Instruments Ultimate 3000 High-Perfor- mance Liquid Chromatograph (1000 mm × 1000 mm × 1000 mm), SIL-20A XR UFLC (Shimadzu, Japan); Triple TOF 5600 System-MS/MS High-Resolution Triple Quadrupole Time of Flight Mass Spectrometer (AB SCEIX, USA); Electronic Balance (Tianjin Tianma Hengji Instrument Co., Ltd.); SHZ-D (III) Circulating Water Vac- uum Pump (Nanjing Wenke Instrument and Equipment Co., Ltd.); KQ-500B Ultrasound Cleaner (Kunshan Ultrasound Instrument Co., Ltd.); PST-JY-10 Puri Phil pure water machine.

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Materials Methanol (TEDIA, batch No. 18095056), formic acid (Jiangsu Qiangsheng Functional Chemi- cal Co., Ltd., batch No. 20160412), and acetonitrile (Merch, batch No. 1.00030.4000). SL medicinal materials were obtained from the TCM pharmacy of Jiangsu Province Hospital of Chinese Medicine and were purchased from Ma’anshan Jingquan Traditional Chinese Medicine Decoction Pieces Co., Ltd. Origin: Zhejiang, Batch No. 200601, Standard Basis: Pharmacopoeia of the People’s Republic of China (2015 Edition). It was identifed by associate professor Ruilian Yu, School of Pharmacy, the Nanjing University of Chinese Medicine as Spargani- aceae plant Sparganium stoloniferum (Buch.-Ham. ex Graebn.) Buch.-Ham. ex Juz. tubers. Te specimens were deposited in the Central Laboratory of Jiangsu Province Hospital of Chinese Medicine.

Preparation of the test solution of SL. Te proper amount of SL medicinal materials was crushed and sieved through 60 mesh, and 1 g of powder was precisely weighed. Ten, the weighed 1 g powder was soaked ten times in double-distilled water for 30 min, refuxed and extracted twice, the frst for 30 min and the second for 20 min, combined with two fltrates, evaporated by a rotary evaporator at 70 ℃, and then reconstituted with absolute ethanol to a 10-ml volumetric fask.

Chromatographic and mass spectrometry conditions. Chromatographic conditions Hedera C18 column (250 mm × 4.6 mm, 5 μm); mobile phase: 0.1% formic acid water (B)—0.1% formic acid methanol (C), gradient elution (0–7 min, 97–97% B; 7–15 min, 97–50% B; 15–20 min, 50–10% B; 20–25 min, 10–97% B; 25–37 min, 97–97% B); fow rate: 1 mL/min, column temperature 30 ℃, injection volume 5 μL. Te detection wavelength of DAD was 260 nm. Mass spectrometry conditions Electron spray ionization (ESI), using positive and negative ion mode scanning; mass scanning range m/z 50–1500; ion source temperature 550 ℃; air curtain gas fow rate 40 L/ min; atomization airfow speed 55 L/min; auxiliary airfow speed 55 L/min; spray voltage + 5500 V/− 4500 V; decluster voltage ± 100 V. Data acquisition sofware: Analyst TF 1.6 sofware (AB SCEIX, USA); data processing sofware: Peakview 1.2 sofware (AB SCEIX, USA).

Identifcation of compounds. According to the multistage mass spectrum fragment information and the pre- cise relative molecular mass provided by high-resolution mass spectrometry, the molecular formula was ftted by Peakview 1.2 sofware with a mass deviation range (δ) ≤ 5 × 10–6, and the compounds were preliminarily predicted. Ten, it was further confrmed by comparing the retention time and the mass spectrum fragment information provided by the SciFinder database and related references to achieve the purpose of the accurate identifcation of compounds.

Network pharmacology research. Prediction of potential targets of compounds and collection of disease targets. SwissTargetPrediction (http://www.swiss​targe​tpred​ictio​n.ch/)60 is a network tool for ligand-based tar- get prediction of any small biologically active molecule. We transformed the compounds identifed by mass spectrometry into canonical SMILES through the PubChem (https​://pubch​em.ncbi.nlm.nih.gov/), Chemical Book (https​://www.chemi​calbo​ok.com/Produ​ctInd​ex.aspx), and ChemSpider (http://www.chems​pider​.com/) databases. We then imported SMILES into SwissTargetPrediction to predict all potential targets of compounds. Species were selected as “Homo sapiens” with probability > 0 as the screening condition. Using “Gastric Cancer” as the keyword, the human gene database (GeneCards, https://www.genec​ ards.org/​ )61, the Online Mendelian Inheritance in Man (OMIM, https://omim.org/​ )62, DisGeNET (Version 7.0) (https://www.​ disge​net.org/)63 and the Terapeutic Target Database (TTD, http://bid.nus.edu.sg/group​/cjttd​/)64 were used to collect relevant targets of GC. In this study, “score” ≥ mean value was used as the criterion for screening disease target genes. Ten, the predicted targets of the chemical components of SL were mapped with the targets of GC, and the intersection of the two was taken to obtain the target set of SL for the treatment of GC.

Construction of compound‑target network. Te chemical components of SL and its therapeutic targets in GC were introduced into Cytoscape (Version 3.8.0) (https​://cytos​cape.org/)65 to construct the compound-target net- work. Te “network analysis” is used to analyse the topological parameters of the network, where the “degree” represents the number of nodes connected with this node in the network; the greater the degree of the node is, the more critical it is in the network. Te “betweenness centrality” refects the importance of a node in transmit- ting information through the network, and the greater the betweenness centrality of the node is, the more critical it is in the network. Te core network was screened based on the network node topological parameters “degree” and “betweenness centrality” to obtain the main active ingredients of SL for the treatment of GC.

Construction of PPI network. Te targets of SL for the treatment of GC were imported into the STRING Data- base (Version 11.0) (https​://strin​g-db.org/)66, and the correlation between target proteins was analysed. “Organ- ism” was set as “Homo sapiens”. Te PPI network was constructed with a “combined score” ≥ 0.9 as the screening condition. Te visualization process was carried out with Cytoscape (Version 3.8.0), and targets with a high degree of connectivity were selected as hub genes.

Gene function annotation and construction of the compound‑target‑pathway network. Te Database for Anno- tation, Visualization and Integrated Discovery (DAVID) (Version 6.8) (https​://david​.ncifc​rf.gov/)67,68 provides systematic and comprehensive biological function annotation information for a large number of genes. It can identify the most signifcantly enriched biological annotations. We introduced the target set of SL for GC treat- ment into DAVID (Version 6.8) and defned the species as “Homo sapiens” for Gene Ontology (GO) and Kyoto

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Encyclopedia of Genes and Genomes (KEGG) pathway analyses. To more comprehensively annotate the bio- logical functions of genes to better understand the molecular mechanism of SL in treating GC, GO will describe the nature of genes from three terms, including cell component (generally used to describe the location of gene action), molecular function (which can describe the activity at the molecular level) and biological process. P < 0.01 was used as a screening condition. Enrichment analysis bubble maps were plotted using the R language. Based on the results of KEGG pathway analysis, pathways related to GC and the top 20 enriched genes were identifed. Ten, Cytoscape (version 3.8.0) was used to further construct the compound-target-pathway network.

Molecular docking between active ingredients and hub genes. To further validate the reliability of the target pre- diction results, molecular docking was performed on the selected active ingredients and hub genes. Active ingre- dients were loaded in the SDF format fle of their 3D structure through the PubChem database and were then imported into Chem3D for optimization and saved in mol2 format; hub genes were kept in the Research Col- laboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB, https​://www.rcsb.org/)69,70, where the best protein crystal structure was selected (human protein, with ligands, relatively complete structure, smaller resolution value), and its PDB format fle was downloaded. Before docking, the original crystal ligand and water molecule in the protein–ligand complex were removed using ­PyMol71. Te protein and ingredients were then hydrogenated, charged, and subjected to other operations using AutoDockTools and converted into PDBQT format fles. Auto Dock ­Vina72 was used to perform molecular docking between the processed ingredients and protein, and the docking results were visualized using PyMol sofware.

External validation of hub genes. Analysis of mRNA expression level. Oncomine 4.5 (https​://www. Oncom​ine.org)73 is a cancer gene expression profle database and integrated data-mining platform designed to facilitate the discovery of genome-wide expression analysis. Trough the Oncomine database, we compared the diferential expression of hub genes in GC tissues and normal gastric tissues. Gene Expression Profling Interactive Analysis (GEPIA, http://gepia​.cancer-pku.cn/index​ .html​ )74 is a newly developed interactive web server for analysing the RNA sequencing expression data of 9736 tumours and 8587 normal samples from the TCGA and GTEx projects using a standard processing pipeline. Te GEPIA database can further verify the diferential expression of hub genes between GC and normal gastric tissues, and it can also analyse them according to pathological stages.

Analysis of protein expression level. Te Human Protein Atlas (Version 19.3) (HPA, https​://www.prote​inatl​ as.org/)75 database is mainly an extensive proteome database based on immunohistochemical analysis. Te pro- tein expression levels of hub genes in GC tissues and normal gastric tissues were compared according to the staining intensity and percentage of stained cells in the tissues, and representative immunohistochemical stain- ing pictures were obtained. Data availability All data generated or analysed during this study are included in this published article and its “Supplementary Information” fles.

Received: 29 September 2020; Accepted: 5 January 2021

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Acknowledgements Tis study was fnancially supported by the National Natural Science Foundation of China (no. 81673918), Pilot Gastric Cancer Project of Clinical Cooperation of Traditional Chinese and Western Medicine for Major and Difcult Diseases, and the Project of evidence-based capacity building of traditional Chinese medicine, Chinese Academy of Traditional Chinese Medicine, State Administration of Traditional Chinese Medicine (no. 2019XZZX-ZL003). Author contributions L.X.N., Z.Y.W., W.F., and S.P. proposed this idea and designed research methods. H.W.J. and G.S.P. analysed the mass spectrometry. C.X.X. and R.S. collated the data. H.Y. and T.Y.H. carried out data analysis and map- ping. L.X.N. and Z.Y.W. wrote and edited the paper. P.S. proofread the manuscript. All authors reviewed the manuscript.

Competing interests Te authors declare no competing interests. Additional information Supplementary Information Te online version contains supplementary material available at https​://doi. org/10.1038/s4159​8-021-81485​-0. Correspondence and requests for materials should be addressed to P.S.

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